Conference paper
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5, 2014
ISPRS Technical Commission V Symposium, 23 – 25 June 2014, Riva del Garda, Italy
HOW TO DECIDE WHICH OBLIQUE IMAGE HAS THE HIGHEST MAPPING
POTENTIAL FOR MONOPLOTTING METHOD: A CASE STUDIES ON RIVER
EROSION AND FLOODS
Mihaela Triglav-Čekada a, Vasja Bric a, Matija Zorn b
b
a
Geodetic institute of Slovenia, Jamova cesta 2, SI-1000 Ljubljana, Slovenia - [email protected]
Anton Melik Geographical Institute, Scientific Research Centre of the Slovenian Academy of Sciences and Arts, Gosposka ulica
13, SI-1000 Ljubljana, Slovenia
Commission V, WG V/5
KEY WORDS: Monoplotting, interactive orientation of images, oblique images, digital elevation models, lidar
ABSTRACT:
When studying the development of different geomorphic processes, floods, glaciers or even cultural heritage through time, one
cannot rely only on regular photogrammetrical procedures and metrical images. In a majority of cases the only available images are
the archive images with unknown parameters of interior orientation showing the object of interest in oblique view. With the help of
modern high resolution digital elevation models derived from aerial or terrestrial laser scanning (lidar) or from photogrammetric
stereo-images by automatic image-matching techniques even single nonmetric high or low oblique image from the past can be
applied in the monoplotting procedure to enable 3D-data extraction of changes through time. The first step of the monoplotting
procedure is the orientation of an image in the space by the help of digital elevation model (DEM). When using oblique images tie
points between an image and DEM are usually too sparse to enable automatic exterior orientation, still the manual interactive
orientation using common features can resolve such shortages. The manual interactive orientation can be very time consuming.
Therefore, before the start of the manual interactive orientation one should be certain if one can expect useful results from the chosen
image. But how to decide which image has the highest mapping potential before we introduce a certain oblique image in orientation
procedure? The test examples presented in this paper enable guidance for the use of monoplotting method for different geoscience
applications. The most important factors are the resolution of digital elevation model (the best are the lidar derived ones), the
presence of appropriate common features and the incidence angle of the oblique images (low oblique images or almost vertical aerial
images are better). First the very oblique example of riverbank erosion on Dragonja river, Slovenija, is presented. Than the test
example of September 2010 floods on Ljubljana moor is discussed. Finally, case study from November 2012 floods is presented.
During November 2012 floods an initiative was launched to gather as much non-metrical images of floods as possible from casual
observers (volunteered image gathering). From all gathered images the guidelines presented before helped to pick out 21 % images
which were used for monoplotting.
1. INTRODUCTION
The lidar digital elevation models (DEM), due to their
accuracy and resolution, did revolutionized possibilities
for application of an old photogrammetric method
mainly called monoplotting (Radwan and Makarovič,
1980; Luhmann et al., 2006). Monoplotting is a
photogrammetric method, which enables 3D data
acquisition from just one image with the help of DEM
of any kind (terrain or surface models). With the DEM
and known interior orientation parameters of camera
used, the exterior orientation parameters of image in
the coordinate system of DEM can be derived. When
exterior orientation parameters are searched manually
this method is called interactive orientation (Rönnholm
et al., 2003; Triglav Čekada et al., 2011). Basically we
are looking for the best agreement between the content
of the image and back-projection of the DEM onto that
image content (Figure 1). In the cases, when suitable or
enough tie points (common features) is hard to find for
automatic exterior orientation, manual interactive
orientation enables good alternative. DEM with smaller
grid sizes enables better results of exterior orientation,
as more detail can be seen on the back-projection of the
DEM.
Monoplotting can be applied to oblique or vertical
aerial imagery (Höhle, 2008; dos Santos et al. 2010),
satellite imagery (Willneff et al., 2010) or terrestrial
imagery (Rönnholm et al., 2003; Triglav-Čekada et al.,
2011; Triglav-Čekada and Radovan, 2013; Bozzini et
al., 2012; Wiesmann et al., 2012). Monoplotting
method is applied also in integrated system called
Pictometry; where different professional aerial
cameras, with some having oblique view, are integrated
in one imaging system. It enables direct georeferencing
and acquisition of data mainly for 3D city modelling
(Höhle, 2008).
Monoplotting has a big mapping potential for the
data acquisition from the archive imagery of cultural
and natural heritage, for the natural disaster monitoring
or studying the development of different geomorphic
processes through time. Oblique imagery monoplotting
was already successfully applied for the study of the
long-time vegetation changes (Bozzini et al. 2012),
long-time glacier changes (Triglav-Čekada et al., 2011;
Triglav-Čekada et al., 2014; Wiesmann et al. 2012),
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
doi:10.5194/isprsannals-II-5-379-2014
379
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5, 2014
ISPRS Technical Commission V Symposium, 23 – 25 June 2014, Riva del Garda, Italy
multitemporal landslide monitoring (Travelletti et al.,
2012) and flood monitoring (Triglav-Čekada and
Radovan, 2013).
Figure 1. Superimposition of 5 m × 5 m digital terrain model
(grey to yellow dots), roads (white lines) and the derived
flood boundaries (blue lines) on the image of floods in
Šoštanj, Slovenia, on November 5 2012 (underlying photo:
Dean Tonkli).
When dealing with natural disaster monitoring by
means of volunteered imagery gathering (from casual
observers), like the November 2012 floods monitoring
in Slovenia (Triglav-Čekada and Radovan, 2013), one
can be stuffed with different terrestrial and even aerial
oblique images (photographs and video clips) of
different qualities, acquired by different unknown
cameras, having different distortions. To decide as
quickly as possible, which images have the biggest
mapping potential, some simple measure is needed to
assess the mapping potential of monoplotting method.
The most important factors, which influence on this
are: the coverage of content in question presented on
the image, incidence angle of the oblique image
resulting in changeable scale and the grid size of the
used DEM.
Therefore the purpose of this paper is to give
simple guidelines for deciding which oblique image has
the biggest mapping potential by monoplotting method.
Through case studies presented in this paper the
proposals are given how to overcome the shortages of
different oblique images.
2. METHOD
The manual interactive orientation of an image using
detailed DEM gives as a result the exterior orientation
parameters of the image (three rotations, the location of
the camera’s perspective centre and the scale factor in
the centre of the image), was already described in detail
in Triglav-Čekada et al. (2011). When the image is
oriented correctly, the superimposed DEM points fit to
the detail of the image very well. The method is the
most suitable for areas with easily recognisable
distinctive topographical features (e.g. hills, mountain
peaks). When applying this method to lowlands, the
interactive orientation based just on DEM is not
possible. As the same procedure of interactive
orientation can also be applied using vector break lines
(Karjalainen et al., 2006) it was decided to amend our
point based interactive orientation with vector data.
Still DEM represents the basis for calculation of backprojection, vector data is attached to DEM points. With
including roads, rivers and creeks as break lines in
interactive orientation also the lowlands can be
correctly manually oriented by this method (Figure 5,
6).
For successful exterior orientation of an image,
also interior orientation parameters of the image need
to be known. The interior parameters could be
computed with self-calibration (Luhmann et al., 2006).
But it is not trivial to compute interior orientation
parameters of archive images due to the lack of control
points. Additionally, when dealing with archive images,
we sometimes can use images, which are not complete.
Old books and magazines can be a valuable source of
archive images, but some published images were
cropped due for better visual effect (better composition
of image content). This unknown extent of image
cropping presents a problem when trying to do selfcalibration. In the case of long-time and multitemporal
studies, e.g. glacier studies, such cropped images might
be the only source to examine the past stages of the
glacier (Triglav Čekada et al., 2014). Therefore, we
included in the procedure of interactive orientation also
a search for distortion parameters. The procedure of
interactive orientation was divided in two steps. First
the best-fit of exterior orientation parameters is
searched for (three rotations, the location of the
camera’s perspective centre and the scale factor).
Afterwards the distortion parameters are introduced
(radial and decentring distortion) again manually. In
both steps the parameters are manually changed,
searching for the best fit of DEM back-projection to the
image content. Original image is not resampled.
3. THE MONOPLOTTING MAPPING POTENCIAL
OF AN SINGLE OBLIQUE IMAGE
The mapping potential of the oblique image depends on
the image content, the varying scale of the image and
possibility to accurately enough orient the image in the
space.
Image content should represent enough details
relevant for the study of geomorphological changes
through time. Those details should be represented in
good resolution and clearly visible. The resolution in
which a detail is presented depends on the image scale.
But it should not be neglected that oblique images have
very varying scale. Therefore the pixel area on the
ground is of different size and form from one to the
other side of the image. On Figure 2 this principle is
presented through back-projection of digital terrain
model (DTM) points.
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
doi:10.5194/isprsannals-II-5-379-2014
380
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5, 2014
ISPRS Technical Commission V Symposium, 23 – 25 June 2014, Riva del Garda, Italy
Figure 2. The DTM superimposition on image content for aerial (left) and oblique image (right).
The varying scale of the image influences also on the
accuracy of the image orientation with the help of the
DEM. Common features used to connect the content of
the image and DEM superimposition should be easily
recognisable in both and should be evenly distributed
through the whole image. When using high oblique
images, the poor scale of some image parts also
disables the identification of good common features in
such parts, therefore worse accuracy of orientation can
be expected.
After we have oriented the image correctly in the
space, the superimposed DEM points will fit very well
to the image content, at least in parts of the image with
the larger scale. Now the process of mapping of our
object of interest can start. The resolution of DEM
again plays a very important role, as we cannot measure
details, which are smaller than the grid of the DEM.
Prior to the acquisition of data from oblique image it
should be decided, which part of the image still has
good enough image resolution and which is hindered
by the poor scale and should therefore not be used for
the acquisition. The mapping should stop where the
superimposed DEM grid length becomes smaller that
the pixel size of the image; marked with red arrow on
Figure 2.
A simple measure to decide if DTM or lidar data
used has good enough resolution for monoplotting is
the cartographic scale (M) in which the mapping results
should be presented. The drawing accuracy (DA) on a
map was defined through the cartographic history as a
minimal line thickness, which can be drawn by a pen to
a sheet of paper representing a map. In general the
drawing accuracy is better than 0.25 mm, with smallest
value of 0.13 mm. For general calculations usually
drawing accuracy of 0.2 mm is used (Maling, 1989).
The drawing accuracy 0.2 mm gives by the Equation 1
for a mapping scale of 1:1000 a geometrical accuracy
of 0.2 m – the size of this error in nature:
GA=DA· M
(1)
The drawing accuracy DA [m] and the desired
map scale M can be connected with grid size of DTM d
[m] by the Equation 2 (Maling, 1989) and with the
average lidar point density [points/m2], when not
dealing with heavily vegetated areas, by Equation 3
(Triglav-Čekada et al., 2010):
d
M DA 10 3
2
1
( M DA 10 3 / 2) 2
(2)
(3)
By Equations 2 and 3 the cartographic scale of 1:5000
needs lidar point density of 4 pt/m2 or DTM with 0.5 m
× 0.5 m grid size (Table 1).
Map scale
1:1000
1:5000
1:10.000
1:50.000
GA
0.2 m
1m
2m
5m
d
0.1 m × 0.1 m
0.5 m × 0.5 m
1m×1m
5m×5m
100 pt/m2
4 pt/m2
1 pt/m2
0.04 pt/m2
Table 1: Map scale, GA – geometrical accuracy, d – grid size
of DTM, – lidar point density.
4. DATA AND RESULTS
4.1 The riverbank erosion of the Dragonja river
Two non-metrical oblique images were used for
measuring riverbank erosion of Dragonja river,
Slovenia, induced by the floods of September 2010.
One image was taken before the floods in 2010 and one
after the floods in the year 2012 (Figure 3 and 4). The
cameras with which the images were taken are not
known, meaning we do not have interior orientation
parameters of these images. The standpoint of the
images was known only approximately and was not the
same for both images.
The lidar data used for monoplotting were
acquired in winter of 2011 at the start of the National
aerial lasers scanning project of Slovenia. Average
overall density of all laser returns at this area was 5
points/m2 and the digital terrain model (DTM) with 1 m
× 1 m grid size was derived from it also. For the
orientation of both images unclassified lidar point
cloud and DTM were used.
Due to the highly oblique images, the manual
interactive absolute orientation resulted in poor
accuracy. As common features (tie patches) only four
trees in the back side of the images were used (marked
on the Figure 3 and 4 with arrows) as well as the flat
riverbed in the forehand of the image. Due to the poor
reflectance of lidar points from water the interpolated
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
doi:10.5194/isprsannals-II-5-379-2014
381
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5, 2014
ISPRS Technical Commission V Symposium, 23 – 25 June 2014, Riva del Garda, Italy
DTM at this area helped a great deal. No lines were
seen on the images, therefore for the orientation only
superimposed lidar points were used. The hills in the
back of the image are too far to help in the orientation.
From the Equation 3 or Table 1 it can be seen that
the lidar data used in this example should enable 1 m
graphical accuracy. But the searched changes were just
a little bigger than expected graphical accuracy and due
to the poor exterior orientation it can be concluded that
results are not reliable.
4.2 September 2010 floods in Ljubljana moor
Figure 3. The riverbank of the Dragonja river before 2010. In
yellow are lidar points near the standpoint and in purple
those almost 110 m away. The points more than 110 m away
from the standpoint are deleted. Only 40 % of the original
points is presented (underlying photo: M. Zorn).
Figure 4. The riverbank of Dragonja river in 2012
(underlying photo: M. Zorn).
The riverbed side erosion can be seen in a form of
group of trees in the middle of the image (Figure 3),
which were eroded away together with riverbank, as
they are not seen anymore on the Figure 4. Therefore at
least 1-2 m planimetric movement of riverbank edge
can be expected.
Due to sparse and very poor distribution of tie
patches, the distortion corrections of the image were
not searched for in the process of interactive
orientation. If we check the fitting of upper riverbank
line on Figure 3 in the at most left position the
superimposed lidar points lie 35 cm above the upper
riverbank line seen on the image, at the at most right
position they are 18 cm above the riverbank line,
respectively. This implies that this image has nonnegligible distortions. As we did not take them into the
account the precision of exterior orientation is poor.
Due to high precitipation between 16 and 19 September
2010 floods affected Ljubljana moor (Komac and Zorn,
2013). The water spilled on almost flat terrain and
stayed there for a couple of days. For a location near
Podpeč we obtained two oblique image sets presenting
the flood in two dates: first were taken by hand from a
hill above Podpeč (Figure 5) on 20 September, and the
second set was taken by hand from a helicopter flying
above the Podpeč on 23 September 2010 (Figure 6).
The images were taken by the same non-metrical
camera Canon PowerShot SX IS. These image sets
enable a study of water recession in time span of three
days. In this paper just one image pair is presented.
For the monoplotting a photogrammetrical DTM
5 m × 5 m and lidar derived DTM 1 m × 1 m (based on
lidar density 10 pt/m2) were used. In the interactive
absolute orientation first the DTM 5 m × 5 m and road
and river vectors were applied. After this, the lidar
DTM was used to introduce small corrections in the
orientation results. For the mapping just a little more
than a lower half of image content was used in both
cases, due to the poor image scale on the other parts of
the image (see the white lines on Figure 6). For the
flood boundary extraction at first lidar DTM was used.
The lines were generalized, taking approximately one
point out of DTM on a length of 5-10 m. To speed up
the monoplotting the DTM 5 m × 5 m was used in
further acquisitions.
Figure 5. Flood on Ljubljana moor flood taken by hand from
a hill above Podpeč on 20 September 2010 with non-metrical
camera (photo: M. Zorn).
We were still able to measure the 2 m ± 2 m
planimetric shift of riverbank between both images.
The big error of the measurement implies that this
result is not very reliable.
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
doi:10.5194/isprsannals-II-5-379-2014
382
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5, 2014
ISPRS Technical Commission V Symposium, 23 – 25 June 2014, Riva del Garda, Italy
Figure 6. Flood on Ljubljana moor taken by hand from a
helicopter on 23 September 2010 with non-metrical camera
(photo: M. Zorn). The flood boundaries acquired from this
image are given as white lines.
× 5 m which covers the whole Slovenia was available.
From the public we received altogether 102 oblique
images and one video taken from higher standpoints
(e.g. from a hilltop) and also from the air. These images
were taken by different cameras having different
resolutions and distortions. To speed up the process of
monoplotting and to produce results as soon as possible
we had to decide which images have the highest
mapping potential for monoplotting process. The next
criteria ware used:
image content should cover as broad area as
possible – to map as big areas as possible
from a single image (see Figure 9 versus
Figure 8)
the best are aerial images or those taken from
high hills – low oblique images
the content of the image should show at least
three or more easily recognisable parts of
roads to speed up the interactive absolute
orientation by the vector break lines
just small image distortions should be visible
on used images
Figure 7. A part of Ljubljana moor flood in September 2010.
On the Figure 7 the extracted flooded areas are
presented on the ortophoto. The white area presents the
flood area on the 20 September. As first image is more
oblique than the second image, the erroneously placed
flood boundary at the upper right corner of Figure 7 is
seen (see the arrow). This boundary should be located
app. 40 m more to the left to coincide with the flood
boundary of 23 September shown in grey. The main
source for this error of location is the changing scale
factor of oblique image, which influences on the
accuracy of image absolute orientation. The used lidar
DTM should enable monoplotting with geometrical
accuracy of 2 m (mapping scale 1:10.000). When using
DTM 5 m × 5 m the geometrical accuracy should still
be 10 m, which is smaller than the 40 m observed.
The used oblique images still enabled the
calculation of flood area recession from 10 ha to 7 ha.
Figure 8. Flood in Tolmin on 5 November 2012 (photo: R.
Lipušček).
4.3 November 2012 floods
Encouraged by the results of flood boundary extraction
from oblique non-metrical images in 2010, the same
technique was applied for 5 and 6 November 2012
floods. An initiative was launched to gather
volunteered images of floods by casual observers.
These floods affected almost the whole upper half of
the Slovenia, being the most severe on 400 km of river
segments (Triglav-Čekada and Radovan, 2013). For the
monoplotting method the photogrammetrical DTM 5 m
Figure 9. Flood in Tolmin on 5 November 2012 (underlying
photo: B. Močnik)
Some 45 % of received images were classified as
potentially useful fulfilling the upper criteria. For the
actual flood mapping 22 images and 12 sequences from
one video were used (e.g. Figure 1). On average around
4 hours was needed for manual interactive orientation
and acquisition of flood border per image.
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
doi:10.5194/isprsannals-II-5-379-2014
383
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5, 2014
ISPRS Technical Commission V Symposium, 23 – 25 June 2014, Riva del Garda, Italy
With the monoplotting method we successfully
mapped 48 km of the most affected river sections or
1439.4 ha of flooded areas (Figure 10).
6. ACKNOWLEDGEMENTS
This work was partly financially supported by the
Slovenian Research Agency projects No. Z6–4182, L64048 and J2-5479.
7. REFERENCES
Bozzini, C., Conedera, M., Krebs, P., 2012. A new monoplotting
tool to extract georeferenced vector data and ortorectified raster
data from oblique non-metric photographs, International
Journal of Heritage in the Digital Era , 1(3), pp. 499-518.
dos Santos, D.R., Dal Poz, A.P., Dalmolin, Q., 2010. Indirect
orientation of images using control points extracted by the
means of monoplotting model, The Photogrammetric Journal
of Finland , 22(1), pp. 60-76.
Figure 10. The most affected river sections (green lines) of
the November 2012 floods in Slovenia and the mapped
flooded areas from volunteered oblique images (pink).
5. CONCLUSION
As the examples 4.1 and 4.2 have shown, the most
important factor, which define what it is possible to
acquire from a single image by the monoplotting
procedure is the DTM or lidar point cloud resolution. It
defines the appropriate map scale in which the results
can be presented. For example if having almost vertical
image and the lidar point cloud with density 5
points/m2, it enables mapping in scale 1:5000 (the
geometrical accuracy of 1 m).
The high-resolution DTM or lidar point cloud
also enable easier tie points definition between the
content of the image and the DTM back-projection.
Good distinguishable tie points have also to be
uniformly distributed on the image, to enable good
results of interactive absolute orientation. The vector
break lines (e.g. streets, river borders) may help a lot
with the orientation.
If an image has non-negligible distortions, the
removal of distortion should also be included in the
process. The image may be resampled to distortion free
image prior to the interactive orientation, or additional
parameters may be added in the process of interactive
orientation, which influence on the DTM backprojection.
When having highly oblique image the influence
of varying scale on the accuracy of interactive
orientation and the possibility to extract details from
the image should not be neglected. The image content
in front of the line where pixel size becomes the same
or smaller as back-projected DTM grid size length
should be used for orientation and acquisition purposes.
These guidelines may help to carry out bigger
project, e.g. processing of a great number of
volunteered images of different natural disasters (e.g.
presented floods of November 2012 in Slovenia in
section 4.3) or studying the geomorphic changes
through time.
Höhle, J., 2008. Photogrammetric measurements in oblique aerial
images. Photogrametry Fernerkundung Geoinformation , 7-14.
Karjalainen, M., Hyyppä, J., Kuittinen, R., 2006. Determination of
exterior orientation using linear features from vector maps, The
Photogrammetric Record , 21(116), pp. 329-341.
Komac, B., Zorn, M., 2013. Extreme floods in Slovenia in
September 2010. In: Geomorphological Impact of Extreme
Weather , Dordrecht: Springer, pp. 121-139.
Luhmann, T., Robson, S., Kyle, S., Harley, I., 2006. Close Range
Photogrammetry: Principles, techniques and applications.
Dunbeath: Whittles Publishinig, 510 p.
Maling, D.H. 1989. Measurements from maps: principles and
methods of cartometry. Oxford: Pergamon press. 577 p.
Radwan, M., Makarovič, B., 1980. Digital mono plotting system:
improvements and tests, XIV Congress of the International
society for photogrammetry, Hamburg, pp. 510-533.
Rönnholm, P., Hyyppä, H., Pöntinen, P., Haggrén, H., Hyyppä, J.
2003. A method for interactive orientation of digital images
using backprojection of 3D data. The Photogrammetric Journal
of Finland , 18(2), pp. 16-31.
Travelletti, J., Delacourt, C., Allemand, P., Malet, J.-P., Schmittbuhl,
J. Taussaint, R., Bastard, M., 2012. Correlation of multitemporal ground-based optical images for landslide monitoring:
Application, potential and limitations, ISPRS Journal of
Photogrammetry and Remote Sensing , 70, pp. 39-55.
Triglav-Čekada, M., Crosilla, F., Kosmatin-Fras, M., 2010.
Theoretical lidar point density for topographic mapping in the
largest scales, Geodetski vestnik, 54(3), pp. 403-416.
Triglav-Čekada, M., Radovan, D., Gabrovec, M., Kosmatin Fras, M.,
2011. Acquisition of the 3D boundary of the Triglav glacier
from archived non-metric panoramic images. The
Photogrammetrical Record , 26(133), pp. 111–129.
Triglav-Čekada, M., Radovan, D., 2013. Using volunteered
geographical information to map the November 2012 floods in
Slovenia, Natural Hazards Earth System Sciences, 13, pp.
2753-2762.
Triglav-Čekada, M., Zorn, M., Colucci, R.R., 2014. Area changes on
Canin (Italy) and Triglav glaciers (Slovenia) from 1893 on
based on archive imagery and lidar, Geodetski vestnik, 58(2), in
press.
Wiesmann, S., Steiner, L., Pozzi, M., Bozzini, C., Bauder, A., Hurni,
L., 2012. Reconstructing historic glacier states based on
terrestrial oblique photographs, Proceedings – AutoCarto 2012
– Columbus, Ohio, USA, September 16-18.
Willneff, J., Poon, J., Fraser, C., 2010. Monoplotting applied to
high-resolution satellite imagery, Journal of Spatial Science ,
50(2), pp. 1-11.
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
doi:10.5194/isprsannals-II-5-379-2014
384
ISPRS Technical Commission V Symposium, 23 – 25 June 2014, Riva del Garda, Italy
HOW TO DECIDE WHICH OBLIQUE IMAGE HAS THE HIGHEST MAPPING
POTENTIAL FOR MONOPLOTTING METHOD: A CASE STUDIES ON RIVER
EROSION AND FLOODS
Mihaela Triglav-Čekada a, Vasja Bric a, Matija Zorn b
b
a
Geodetic institute of Slovenia, Jamova cesta 2, SI-1000 Ljubljana, Slovenia - [email protected]
Anton Melik Geographical Institute, Scientific Research Centre of the Slovenian Academy of Sciences and Arts, Gosposka ulica
13, SI-1000 Ljubljana, Slovenia
Commission V, WG V/5
KEY WORDS: Monoplotting, interactive orientation of images, oblique images, digital elevation models, lidar
ABSTRACT:
When studying the development of different geomorphic processes, floods, glaciers or even cultural heritage through time, one
cannot rely only on regular photogrammetrical procedures and metrical images. In a majority of cases the only available images are
the archive images with unknown parameters of interior orientation showing the object of interest in oblique view. With the help of
modern high resolution digital elevation models derived from aerial or terrestrial laser scanning (lidar) or from photogrammetric
stereo-images by automatic image-matching techniques even single nonmetric high or low oblique image from the past can be
applied in the monoplotting procedure to enable 3D-data extraction of changes through time. The first step of the monoplotting
procedure is the orientation of an image in the space by the help of digital elevation model (DEM). When using oblique images tie
points between an image and DEM are usually too sparse to enable automatic exterior orientation, still the manual interactive
orientation using common features can resolve such shortages. The manual interactive orientation can be very time consuming.
Therefore, before the start of the manual interactive orientation one should be certain if one can expect useful results from the chosen
image. But how to decide which image has the highest mapping potential before we introduce a certain oblique image in orientation
procedure? The test examples presented in this paper enable guidance for the use of monoplotting method for different geoscience
applications. The most important factors are the resolution of digital elevation model (the best are the lidar derived ones), the
presence of appropriate common features and the incidence angle of the oblique images (low oblique images or almost vertical aerial
images are better). First the very oblique example of riverbank erosion on Dragonja river, Slovenija, is presented. Than the test
example of September 2010 floods on Ljubljana moor is discussed. Finally, case study from November 2012 floods is presented.
During November 2012 floods an initiative was launched to gather as much non-metrical images of floods as possible from casual
observers (volunteered image gathering). From all gathered images the guidelines presented before helped to pick out 21 % images
which were used for monoplotting.
1. INTRODUCTION
The lidar digital elevation models (DEM), due to their
accuracy and resolution, did revolutionized possibilities
for application of an old photogrammetric method
mainly called monoplotting (Radwan and Makarovič,
1980; Luhmann et al., 2006). Monoplotting is a
photogrammetric method, which enables 3D data
acquisition from just one image with the help of DEM
of any kind (terrain or surface models). With the DEM
and known interior orientation parameters of camera
used, the exterior orientation parameters of image in
the coordinate system of DEM can be derived. When
exterior orientation parameters are searched manually
this method is called interactive orientation (Rönnholm
et al., 2003; Triglav Čekada et al., 2011). Basically we
are looking for the best agreement between the content
of the image and back-projection of the DEM onto that
image content (Figure 1). In the cases, when suitable or
enough tie points (common features) is hard to find for
automatic exterior orientation, manual interactive
orientation enables good alternative. DEM with smaller
grid sizes enables better results of exterior orientation,
as more detail can be seen on the back-projection of the
DEM.
Monoplotting can be applied to oblique or vertical
aerial imagery (Höhle, 2008; dos Santos et al. 2010),
satellite imagery (Willneff et al., 2010) or terrestrial
imagery (Rönnholm et al., 2003; Triglav-Čekada et al.,
2011; Triglav-Čekada and Radovan, 2013; Bozzini et
al., 2012; Wiesmann et al., 2012). Monoplotting
method is applied also in integrated system called
Pictometry; where different professional aerial
cameras, with some having oblique view, are integrated
in one imaging system. It enables direct georeferencing
and acquisition of data mainly for 3D city modelling
(Höhle, 2008).
Monoplotting has a big mapping potential for the
data acquisition from the archive imagery of cultural
and natural heritage, for the natural disaster monitoring
or studying the development of different geomorphic
processes through time. Oblique imagery monoplotting
was already successfully applied for the study of the
long-time vegetation changes (Bozzini et al. 2012),
long-time glacier changes (Triglav-Čekada et al., 2011;
Triglav-Čekada et al., 2014; Wiesmann et al. 2012),
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doi:10.5194/isprsannals-II-5-379-2014
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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5, 2014
ISPRS Technical Commission V Symposium, 23 – 25 June 2014, Riva del Garda, Italy
multitemporal landslide monitoring (Travelletti et al.,
2012) and flood monitoring (Triglav-Čekada and
Radovan, 2013).
Figure 1. Superimposition of 5 m × 5 m digital terrain model
(grey to yellow dots), roads (white lines) and the derived
flood boundaries (blue lines) on the image of floods in
Šoštanj, Slovenia, on November 5 2012 (underlying photo:
Dean Tonkli).
When dealing with natural disaster monitoring by
means of volunteered imagery gathering (from casual
observers), like the November 2012 floods monitoring
in Slovenia (Triglav-Čekada and Radovan, 2013), one
can be stuffed with different terrestrial and even aerial
oblique images (photographs and video clips) of
different qualities, acquired by different unknown
cameras, having different distortions. To decide as
quickly as possible, which images have the biggest
mapping potential, some simple measure is needed to
assess the mapping potential of monoplotting method.
The most important factors, which influence on this
are: the coverage of content in question presented on
the image, incidence angle of the oblique image
resulting in changeable scale and the grid size of the
used DEM.
Therefore the purpose of this paper is to give
simple guidelines for deciding which oblique image has
the biggest mapping potential by monoplotting method.
Through case studies presented in this paper the
proposals are given how to overcome the shortages of
different oblique images.
2. METHOD
The manual interactive orientation of an image using
detailed DEM gives as a result the exterior orientation
parameters of the image (three rotations, the location of
the camera’s perspective centre and the scale factor in
the centre of the image), was already described in detail
in Triglav-Čekada et al. (2011). When the image is
oriented correctly, the superimposed DEM points fit to
the detail of the image very well. The method is the
most suitable for areas with easily recognisable
distinctive topographical features (e.g. hills, mountain
peaks). When applying this method to lowlands, the
interactive orientation based just on DEM is not
possible. As the same procedure of interactive
orientation can also be applied using vector break lines
(Karjalainen et al., 2006) it was decided to amend our
point based interactive orientation with vector data.
Still DEM represents the basis for calculation of backprojection, vector data is attached to DEM points. With
including roads, rivers and creeks as break lines in
interactive orientation also the lowlands can be
correctly manually oriented by this method (Figure 5,
6).
For successful exterior orientation of an image,
also interior orientation parameters of the image need
to be known. The interior parameters could be
computed with self-calibration (Luhmann et al., 2006).
But it is not trivial to compute interior orientation
parameters of archive images due to the lack of control
points. Additionally, when dealing with archive images,
we sometimes can use images, which are not complete.
Old books and magazines can be a valuable source of
archive images, but some published images were
cropped due for better visual effect (better composition
of image content). This unknown extent of image
cropping presents a problem when trying to do selfcalibration. In the case of long-time and multitemporal
studies, e.g. glacier studies, such cropped images might
be the only source to examine the past stages of the
glacier (Triglav Čekada et al., 2014). Therefore, we
included in the procedure of interactive orientation also
a search for distortion parameters. The procedure of
interactive orientation was divided in two steps. First
the best-fit of exterior orientation parameters is
searched for (three rotations, the location of the
camera’s perspective centre and the scale factor).
Afterwards the distortion parameters are introduced
(radial and decentring distortion) again manually. In
both steps the parameters are manually changed,
searching for the best fit of DEM back-projection to the
image content. Original image is not resampled.
3. THE MONOPLOTTING MAPPING POTENCIAL
OF AN SINGLE OBLIQUE IMAGE
The mapping potential of the oblique image depends on
the image content, the varying scale of the image and
possibility to accurately enough orient the image in the
space.
Image content should represent enough details
relevant for the study of geomorphological changes
through time. Those details should be represented in
good resolution and clearly visible. The resolution in
which a detail is presented depends on the image scale.
But it should not be neglected that oblique images have
very varying scale. Therefore the pixel area on the
ground is of different size and form from one to the
other side of the image. On Figure 2 this principle is
presented through back-projection of digital terrain
model (DTM) points.
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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5, 2014
ISPRS Technical Commission V Symposium, 23 – 25 June 2014, Riva del Garda, Italy
Figure 2. The DTM superimposition on image content for aerial (left) and oblique image (right).
The varying scale of the image influences also on the
accuracy of the image orientation with the help of the
DEM. Common features used to connect the content of
the image and DEM superimposition should be easily
recognisable in both and should be evenly distributed
through the whole image. When using high oblique
images, the poor scale of some image parts also
disables the identification of good common features in
such parts, therefore worse accuracy of orientation can
be expected.
After we have oriented the image correctly in the
space, the superimposed DEM points will fit very well
to the image content, at least in parts of the image with
the larger scale. Now the process of mapping of our
object of interest can start. The resolution of DEM
again plays a very important role, as we cannot measure
details, which are smaller than the grid of the DEM.
Prior to the acquisition of data from oblique image it
should be decided, which part of the image still has
good enough image resolution and which is hindered
by the poor scale and should therefore not be used for
the acquisition. The mapping should stop where the
superimposed DEM grid length becomes smaller that
the pixel size of the image; marked with red arrow on
Figure 2.
A simple measure to decide if DTM or lidar data
used has good enough resolution for monoplotting is
the cartographic scale (M) in which the mapping results
should be presented. The drawing accuracy (DA) on a
map was defined through the cartographic history as a
minimal line thickness, which can be drawn by a pen to
a sheet of paper representing a map. In general the
drawing accuracy is better than 0.25 mm, with smallest
value of 0.13 mm. For general calculations usually
drawing accuracy of 0.2 mm is used (Maling, 1989).
The drawing accuracy 0.2 mm gives by the Equation 1
for a mapping scale of 1:1000 a geometrical accuracy
of 0.2 m – the size of this error in nature:
GA=DA· M
(1)
The drawing accuracy DA [m] and the desired
map scale M can be connected with grid size of DTM d
[m] by the Equation 2 (Maling, 1989) and with the
average lidar point density [points/m2], when not
dealing with heavily vegetated areas, by Equation 3
(Triglav-Čekada et al., 2010):
d
M DA 10 3
2
1
( M DA 10 3 / 2) 2
(2)
(3)
By Equations 2 and 3 the cartographic scale of 1:5000
needs lidar point density of 4 pt/m2 or DTM with 0.5 m
× 0.5 m grid size (Table 1).
Map scale
1:1000
1:5000
1:10.000
1:50.000
GA
0.2 m
1m
2m
5m
d
0.1 m × 0.1 m
0.5 m × 0.5 m
1m×1m
5m×5m
100 pt/m2
4 pt/m2
1 pt/m2
0.04 pt/m2
Table 1: Map scale, GA – geometrical accuracy, d – grid size
of DTM, – lidar point density.
4. DATA AND RESULTS
4.1 The riverbank erosion of the Dragonja river
Two non-metrical oblique images were used for
measuring riverbank erosion of Dragonja river,
Slovenia, induced by the floods of September 2010.
One image was taken before the floods in 2010 and one
after the floods in the year 2012 (Figure 3 and 4). The
cameras with which the images were taken are not
known, meaning we do not have interior orientation
parameters of these images. The standpoint of the
images was known only approximately and was not the
same for both images.
The lidar data used for monoplotting were
acquired in winter of 2011 at the start of the National
aerial lasers scanning project of Slovenia. Average
overall density of all laser returns at this area was 5
points/m2 and the digital terrain model (DTM) with 1 m
× 1 m grid size was derived from it also. For the
orientation of both images unclassified lidar point
cloud and DTM were used.
Due to the highly oblique images, the manual
interactive absolute orientation resulted in poor
accuracy. As common features (tie patches) only four
trees in the back side of the images were used (marked
on the Figure 3 and 4 with arrows) as well as the flat
riverbed in the forehand of the image. Due to the poor
reflectance of lidar points from water the interpolated
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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5, 2014
ISPRS Technical Commission V Symposium, 23 – 25 June 2014, Riva del Garda, Italy
DTM at this area helped a great deal. No lines were
seen on the images, therefore for the orientation only
superimposed lidar points were used. The hills in the
back of the image are too far to help in the orientation.
From the Equation 3 or Table 1 it can be seen that
the lidar data used in this example should enable 1 m
graphical accuracy. But the searched changes were just
a little bigger than expected graphical accuracy and due
to the poor exterior orientation it can be concluded that
results are not reliable.
4.2 September 2010 floods in Ljubljana moor
Figure 3. The riverbank of the Dragonja river before 2010. In
yellow are lidar points near the standpoint and in purple
those almost 110 m away. The points more than 110 m away
from the standpoint are deleted. Only 40 % of the original
points is presented (underlying photo: M. Zorn).
Figure 4. The riverbank of Dragonja river in 2012
(underlying photo: M. Zorn).
The riverbed side erosion can be seen in a form of
group of trees in the middle of the image (Figure 3),
which were eroded away together with riverbank, as
they are not seen anymore on the Figure 4. Therefore at
least 1-2 m planimetric movement of riverbank edge
can be expected.
Due to sparse and very poor distribution of tie
patches, the distortion corrections of the image were
not searched for in the process of interactive
orientation. If we check the fitting of upper riverbank
line on Figure 3 in the at most left position the
superimposed lidar points lie 35 cm above the upper
riverbank line seen on the image, at the at most right
position they are 18 cm above the riverbank line,
respectively. This implies that this image has nonnegligible distortions. As we did not take them into the
account the precision of exterior orientation is poor.
Due to high precitipation between 16 and 19 September
2010 floods affected Ljubljana moor (Komac and Zorn,
2013). The water spilled on almost flat terrain and
stayed there for a couple of days. For a location near
Podpeč we obtained two oblique image sets presenting
the flood in two dates: first were taken by hand from a
hill above Podpeč (Figure 5) on 20 September, and the
second set was taken by hand from a helicopter flying
above the Podpeč on 23 September 2010 (Figure 6).
The images were taken by the same non-metrical
camera Canon PowerShot SX IS. These image sets
enable a study of water recession in time span of three
days. In this paper just one image pair is presented.
For the monoplotting a photogrammetrical DTM
5 m × 5 m and lidar derived DTM 1 m × 1 m (based on
lidar density 10 pt/m2) were used. In the interactive
absolute orientation first the DTM 5 m × 5 m and road
and river vectors were applied. After this, the lidar
DTM was used to introduce small corrections in the
orientation results. For the mapping just a little more
than a lower half of image content was used in both
cases, due to the poor image scale on the other parts of
the image (see the white lines on Figure 6). For the
flood boundary extraction at first lidar DTM was used.
The lines were generalized, taking approximately one
point out of DTM on a length of 5-10 m. To speed up
the monoplotting the DTM 5 m × 5 m was used in
further acquisitions.
Figure 5. Flood on Ljubljana moor flood taken by hand from
a hill above Podpeč on 20 September 2010 with non-metrical
camera (photo: M. Zorn).
We were still able to measure the 2 m ± 2 m
planimetric shift of riverbank between both images.
The big error of the measurement implies that this
result is not very reliable.
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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5, 2014
ISPRS Technical Commission V Symposium, 23 – 25 June 2014, Riva del Garda, Italy
Figure 6. Flood on Ljubljana moor taken by hand from a
helicopter on 23 September 2010 with non-metrical camera
(photo: M. Zorn). The flood boundaries acquired from this
image are given as white lines.
× 5 m which covers the whole Slovenia was available.
From the public we received altogether 102 oblique
images and one video taken from higher standpoints
(e.g. from a hilltop) and also from the air. These images
were taken by different cameras having different
resolutions and distortions. To speed up the process of
monoplotting and to produce results as soon as possible
we had to decide which images have the highest
mapping potential for monoplotting process. The next
criteria ware used:
image content should cover as broad area as
possible – to map as big areas as possible
from a single image (see Figure 9 versus
Figure 8)
the best are aerial images or those taken from
high hills – low oblique images
the content of the image should show at least
three or more easily recognisable parts of
roads to speed up the interactive absolute
orientation by the vector break lines
just small image distortions should be visible
on used images
Figure 7. A part of Ljubljana moor flood in September 2010.
On the Figure 7 the extracted flooded areas are
presented on the ortophoto. The white area presents the
flood area on the 20 September. As first image is more
oblique than the second image, the erroneously placed
flood boundary at the upper right corner of Figure 7 is
seen (see the arrow). This boundary should be located
app. 40 m more to the left to coincide with the flood
boundary of 23 September shown in grey. The main
source for this error of location is the changing scale
factor of oblique image, which influences on the
accuracy of image absolute orientation. The used lidar
DTM should enable monoplotting with geometrical
accuracy of 2 m (mapping scale 1:10.000). When using
DTM 5 m × 5 m the geometrical accuracy should still
be 10 m, which is smaller than the 40 m observed.
The used oblique images still enabled the
calculation of flood area recession from 10 ha to 7 ha.
Figure 8. Flood in Tolmin on 5 November 2012 (photo: R.
Lipušček).
4.3 November 2012 floods
Encouraged by the results of flood boundary extraction
from oblique non-metrical images in 2010, the same
technique was applied for 5 and 6 November 2012
floods. An initiative was launched to gather
volunteered images of floods by casual observers.
These floods affected almost the whole upper half of
the Slovenia, being the most severe on 400 km of river
segments (Triglav-Čekada and Radovan, 2013). For the
monoplotting method the photogrammetrical DTM 5 m
Figure 9. Flood in Tolmin on 5 November 2012 (underlying
photo: B. Močnik)
Some 45 % of received images were classified as
potentially useful fulfilling the upper criteria. For the
actual flood mapping 22 images and 12 sequences from
one video were used (e.g. Figure 1). On average around
4 hours was needed for manual interactive orientation
and acquisition of flood border per image.
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doi:10.5194/isprsannals-II-5-379-2014
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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5, 2014
ISPRS Technical Commission V Symposium, 23 – 25 June 2014, Riva del Garda, Italy
With the monoplotting method we successfully
mapped 48 km of the most affected river sections or
1439.4 ha of flooded areas (Figure 10).
6. ACKNOWLEDGEMENTS
This work was partly financially supported by the
Slovenian Research Agency projects No. Z6–4182, L64048 and J2-5479.
7. REFERENCES
Bozzini, C., Conedera, M., Krebs, P., 2012. A new monoplotting
tool to extract georeferenced vector data and ortorectified raster
data from oblique non-metric photographs, International
Journal of Heritage in the Digital Era , 1(3), pp. 499-518.
dos Santos, D.R., Dal Poz, A.P., Dalmolin, Q., 2010. Indirect
orientation of images using control points extracted by the
means of monoplotting model, The Photogrammetric Journal
of Finland , 22(1), pp. 60-76.
Figure 10. The most affected river sections (green lines) of
the November 2012 floods in Slovenia and the mapped
flooded areas from volunteered oblique images (pink).
5. CONCLUSION
As the examples 4.1 and 4.2 have shown, the most
important factor, which define what it is possible to
acquire from a single image by the monoplotting
procedure is the DTM or lidar point cloud resolution. It
defines the appropriate map scale in which the results
can be presented. For example if having almost vertical
image and the lidar point cloud with density 5
points/m2, it enables mapping in scale 1:5000 (the
geometrical accuracy of 1 m).
The high-resolution DTM or lidar point cloud
also enable easier tie points definition between the
content of the image and the DTM back-projection.
Good distinguishable tie points have also to be
uniformly distributed on the image, to enable good
results of interactive absolute orientation. The vector
break lines (e.g. streets, river borders) may help a lot
with the orientation.
If an image has non-negligible distortions, the
removal of distortion should also be included in the
process. The image may be resampled to distortion free
image prior to the interactive orientation, or additional
parameters may be added in the process of interactive
orientation, which influence on the DTM backprojection.
When having highly oblique image the influence
of varying scale on the accuracy of interactive
orientation and the possibility to extract details from
the image should not be neglected. The image content
in front of the line where pixel size becomes the same
or smaller as back-projected DTM grid size length
should be used for orientation and acquisition purposes.
These guidelines may help to carry out bigger
project, e.g. processing of a great number of
volunteered images of different natural disasters (e.g.
presented floods of November 2012 in Slovenia in
section 4.3) or studying the geomorphic changes
through time.
Höhle, J., 2008. Photogrammetric measurements in oblique aerial
images. Photogrametry Fernerkundung Geoinformation , 7-14.
Karjalainen, M., Hyyppä, J., Kuittinen, R., 2006. Determination of
exterior orientation using linear features from vector maps, The
Photogrammetric Record , 21(116), pp. 329-341.
Komac, B., Zorn, M., 2013. Extreme floods in Slovenia in
September 2010. In: Geomorphological Impact of Extreme
Weather , Dordrecht: Springer, pp. 121-139.
Luhmann, T., Robson, S., Kyle, S., Harley, I., 2006. Close Range
Photogrammetry: Principles, techniques and applications.
Dunbeath: Whittles Publishinig, 510 p.
Maling, D.H. 1989. Measurements from maps: principles and
methods of cartometry. Oxford: Pergamon press. 577 p.
Radwan, M., Makarovič, B., 1980. Digital mono plotting system:
improvements and tests, XIV Congress of the International
society for photogrammetry, Hamburg, pp. 510-533.
Rönnholm, P., Hyyppä, H., Pöntinen, P., Haggrén, H., Hyyppä, J.
2003. A method for interactive orientation of digital images
using backprojection of 3D data. The Photogrammetric Journal
of Finland , 18(2), pp. 16-31.
Travelletti, J., Delacourt, C., Allemand, P., Malet, J.-P., Schmittbuhl,
J. Taussaint, R., Bastard, M., 2012. Correlation of multitemporal ground-based optical images for landslide monitoring:
Application, potential and limitations, ISPRS Journal of
Photogrammetry and Remote Sensing , 70, pp. 39-55.
Triglav-Čekada, M., Crosilla, F., Kosmatin-Fras, M., 2010.
Theoretical lidar point density for topographic mapping in the
largest scales, Geodetski vestnik, 54(3), pp. 403-416.
Triglav-Čekada, M., Radovan, D., Gabrovec, M., Kosmatin Fras, M.,
2011. Acquisition of the 3D boundary of the Triglav glacier
from archived non-metric panoramic images. The
Photogrammetrical Record , 26(133), pp. 111–129.
Triglav-Čekada, M., Radovan, D., 2013. Using volunteered
geographical information to map the November 2012 floods in
Slovenia, Natural Hazards Earth System Sciences, 13, pp.
2753-2762.
Triglav-Čekada, M., Zorn, M., Colucci, R.R., 2014. Area changes on
Canin (Italy) and Triglav glaciers (Slovenia) from 1893 on
based on archive imagery and lidar, Geodetski vestnik, 58(2), in
press.
Wiesmann, S., Steiner, L., Pozzi, M., Bozzini, C., Bauder, A., Hurni,
L., 2012. Reconstructing historic glacier states based on
terrestrial oblique photographs, Proceedings – AutoCarto 2012
– Columbus, Ohio, USA, September 16-18.
Willneff, J., Poon, J., Fraser, C., 2010. Monoplotting applied to
high-resolution satellite imagery, Journal of Spatial Science ,
50(2), pp. 1-11.
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
doi:10.5194/isprsannals-II-5-379-2014
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